Strand 12 Stand-alone Paper Set
- Presenter(s): Xuan-Cuong Trinh; Taesoo An; Xinyu He; Christine Schlendorf
- Session Length: 90 minutes
- Date: Apr 9, 2026
50 DIFFERENCES IN TEACHERS' ARTIFICIAL INTELLIGENCE BEHAVIORAL INTENTIONS: A COMPARATIVE STUDY OF VIETNAM AND TAIWAN
Xuan-Cuong Trinh ORCID iD; Shih-Wen Chen ORCID iD; Xuyen Nguyen Thi My ORCID iD
National Dong Hwa University; Hualien; Taiwan
Abstract
Despite rapid implementation of policies to integrate Artificial Intelligence (AI) into education; clear evidence on teachers' motivations for AI adoption in cross-national contexts remains scarce. Using the Unified Theory of Acceptance and Use of Technology (UTAUT); this study assesses determinants of teachers' behavioral intentions (BI) to adopt AI in education. Survey data were collected from secondary school teachers in Vietnam (n = 358) and Taiwan (n = 345) and analyzed through multi-group structural equation modeling (SEM) with measurement invariance testing to ensure valid comparisons. The results show that facilitating conditions significantly affect intention in both countries; with a stronger effect in Taiwan; underscoring the importance of access to resources and governmental support. For Taiwanese teachers; effort expectancy plays a role as the most powerful factor; whereas performance expectancy and social influence play a larger role among Vietnamese teachers. These cross-country patterns are consistent with differences in educational systems and AI policy advancement and point to the need for teacher support programs and localized strategies for each country.
Strand 12: Technology for Teaching; Learning; and Research
303 Core Competencies of Teachers in the Age of AI: Teacher as Designer in Science Education
taesoo an ORCID iD; Sonya Nicole Martin ORCID iD
Seoul National University; Seoul; Korea; Republic of
Abstract
This conceptual paper proposes a framework for two core teacher competencies; digital integration and automation design; that are essential for meaningful and ethical AI integration in inquiry-based science classrooms. Drawing on scholarly literature; global policy documents (e.g.; UNESCO; OECD); and illustrative classroom cases from the United States; South Korea; and Turkey; the paper explores how teachers can move beyond passive tool use to become designers of AI-supported instruction. The conceptual framework integrates TPACK; AI literacy; and design-based pedagogy to examine how teachers maintain pedagogical agency while leveraging AI tools. Thematic synthesis of literature and cases reveals how these competencies support formative assessment; workflow automation; and responsive feedback while addressing algorithmic bias and contextual equity. This work emphasizes that AI integration may risk instructional standardization and inequity unless accompanied by strong teacher design capacities. The paper contributes a theoretical foundation for teacher education; professional learning; and policy; aiming to empower science teachers to critically and creatively shape AI use in ways that promote inquiry; diversity; and pedagogical integrity.
Strand 12: Technology for Teaching; Learning; and Research
603 When AI is Involved: An Exploratory Study of Pre-service Teachers Planning Science Lessons with ChatGPT
Xinyu He1;2; Emily Adah Miller1; Tingting Li3
1University of Georgia; USA. 2Central China Normal University; China. 3Washington State University; USA
Abstract
This study examines how ChatGPT; as a representative form of AI; influences pre-service science teachers' (PSTs) collaborative lesson planning in a science methods course. Grounded in sociocultural perspectives on learning and Strasser's (2022) human–machine interaction framework; the analysis applies two lenses: tool use and social interaction. Eighteen PSTs in six groups planned a week-long socio-scientific issue unit on hurricanes; three groups were selected for in-depth analysis; including two that used ChatGPT and one that did not. Findings present the dynamic comparison between groups using ChatGPT and the group that didn't. These insights suggest that in AI-era learning; emphasis may shift from final products toward the processes through which learners interact with each other and focus on their own expectations. The study contributes a dual-lens framework for analyzing AI-mediated learning and informs discussions on AI's role in science education.
Strand 12: Technology for Teaching; Learning; and Research
86 An Open versus Commercial Digital Biology Textbook: Student Performance; Access; Use and Textbook Purchasing Trends
Christine Schlendorf ORCID iD; Janaiyah Hipps; Denise Lopez
Farmingdale State College; Farmingdale; NY; USA
Abstract
This study used a mixed-methods approach to explore student performance data (N=164) and survey data (N=140) to examine differences in student performance; textbook use; and access when using a commercial digital textbook compared to an open access digital textbook in an introductory biology college course. A total of 164 students were used in the study; approximately half using the open access digital textbook; while the other half used the commercial digital textbook. Course performance was measured by comparing the average of four exams. To measure differences in textbook usage; access and purchasing trends; a survey was administered at the end of the semester. Results revealed that there was no significant difference in course performance when using the commercial digital textbook versus an open access digital textbook. However; survey data revealed that the open access digital textbook had a better overall rating with a medium to large effect size. Regarding accessibility to the various digital textbooks; there was no significant difference in textbook use whether students were using the commercial digital textbook or open access digital textbook. Finally; results from this study found that course textbook costs can adversely affect student success in a college science course.
Strand 12: Technology for Teaching; Learning; and Research
description
50 DIFFERENCES IN TEACHERS' ARTIFICIAL INTELLIGENCE BEHAVIORAL INTENTIONS: A COMPARATIVE STUDY OF VIETNAM AND TAIWAN
Xuan-Cuong Trinh ORCID iD; Shih-Wen Chen ORCID iD; Xuyen Nguyen Thi My ORCID iD
National Dong Hwa University; Hualien; Taiwan
Abstract
Despite rapid implementation of policies to integrate Artificial Intelligence (AI) into education; clear evidence on teachers' motivations for AI adoption in cross-national contexts remains scarce. Using the Unified Theory of Acceptance and Use of Technology (UTAUT); this study assesses determinants of teachers' behavioral intentions (BI) to adopt AI in education. Survey data were collected from secondary school teachers in Vietnam (n = 358) and Taiwan (n = 345) and analyzed through multi-group structural equation modeling (SEM) with measurement invariance testing to ensure valid comparisons. The results show that facilitating conditions significantly affect intention in both countries; with a stronger effect in Taiwan; underscoring the importance of access to resources and governmental support. For Taiwanese teachers; effort expectancy plays a role as the most powerful factor; whereas performance expectancy and social influence play a larger role among Vietnamese teachers. These cross-country patterns are consistent with differences in educational systems and AI policy advancement and point to the need for teacher support programs and localized strategies for each country.
Strand 12: Technology for Teaching; Learning; and Research
303 Core Competencies of Teachers in the Age of AI: Teacher as Designer in Science Education
taesoo an ORCID iD; Sonya Nicole Martin ORCID iD
Seoul National University; Seoul; Korea; Republic of
Abstract
This conceptual paper proposes a framework for two core teacher competencies; digital integration and automation design; that are essential for meaningful and ethical AI integration in inquiry-based science classrooms. Drawing on scholarly literature; global policy documents (e.g.; UNESCO; OECD); and illustrative classroom cases from the United States; South Korea; and Turkey; the paper explores how teachers can move beyond passive tool use to become designers of AI-supported instruction. The conceptual framework integrates TPACK; AI literacy; and design-based pedagogy to examine how teachers maintain pedagogical agency while leveraging AI tools. Thematic synthesis of literature and cases reveals how these competencies support formative assessment; workflow automation; and responsive feedback while addressing algorithmic bias and contextual equity. This work emphasizes that AI integration may risk instructional standardization and inequity unless accompanied by strong teacher design capacities. The paper contributes a theoretical foundation for teacher education; professional learning; and policy; aiming to empower science teachers to critically and creatively shape AI use in ways that promote inquiry; diversity; and pedagogical integrity.
Strand 12: Technology for Teaching; Learning; and Research
603 When AI is Involved: An Exploratory Study of Pre-service Teachers Planning Science Lessons with ChatGPT
Xinyu He1;2; Emily Adah Miller1; Tingting Li3
1University of Georgia; USA. 2Central China Normal University; China. 3Washington State University; USA
Abstract
This study examines how ChatGPT; as a representative form of AI; influences pre-service science teachers' (PSTs) collaborative lesson planning in a science methods course. Grounded in sociocultural perspectives on learning and Strasser's (2022) human–machine interaction framework; the analysis applies two lenses: tool use and social interaction. Eighteen PSTs in six groups planned a week-long socio-scientific issue unit on hurricanes; three groups were selected for in-depth analysis; including two that used ChatGPT and one that did not. Findings present the dynamic comparison between groups using ChatGPT and the group that didn't. These insights suggest that in AI-era learning; emphasis may shift from final products toward the processes through which learners interact with each other and focus on their own expectations. The study contributes a dual-lens framework for analyzing AI-mediated learning and informs discussions on AI's role in science education.
Strand 12: Technology for Teaching; Learning; and Research
86 An Open versus Commercial Digital Biology Textbook: Student Performance; Access; Use and Textbook Purchasing Trends
Christine Schlendorf ORCID iD; Janaiyah Hipps; Denise Lopez
Farmingdale State College; Farmingdale; NY; USA
Abstract
This study used a mixed-methods approach to explore student performance data (N=164) and survey data (N=140) to examine differences in student performance; textbook use; and access when using a commercial digital textbook compared to an open access digital textbook in an introductory biology college course. A total of 164 students were used in the study; approximately half using the open access digital textbook; while the other half used the commercial digital textbook. Course performance was measured by comparing the average of four exams. To measure differences in textbook usage; access and purchasing trends; a survey was administered at the end of the semester. Results revealed that there was no significant difference in course performance when using the commercial digital textbook versus an open access digital textbook. However; survey data revealed that the open access digital textbook had a better overall rating with a medium to large effect size. Regarding accessibility to the various digital textbooks; there was no significant difference in textbook use whether students were using the commercial digital textbook or open access digital textbook. Finally; results from this study found that course textbook costs can adversely affect student success in a college science course.
Strand 12: Technology for Teaching; Learning; and Research
